ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2019
Towards a universal MRI atlas of the prostate and prostate zones : Comparison of MRI vendor and image acquisition parameters.
Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Automatic segmentation of the urethra and prostate zones with deep learning on T2-weighted magnetic resonance imaging.Physics and imaging in radiation oncology · 2026Article
- Review of different convolutional neural networks used in segmentation of prostate during fusion biopsy.Central European journal of urology · 2025Review
- Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.Abdominal radiology (New York) · 2024Article
- Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature.Insights into imaging · 2022Article
- Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer.Frontiers in oncology · 2022Article
- Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology.Insights into imaging · 2021Article
- Diagnostic efficiency of hybrid imaging using PSMA ligands, PET/CT, PET/MRI and MRI in identifying malignant prostate lesions.Annals of nuclear medicine · 2021Article
- The role of radiomics in prostate cancer radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Review
- Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Article
- Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
background and purposeThe aim of this study was to evaluate an automatic multi-atlas-based segmentation method for generating prostate, peripheral (PZ), and transition zone (TZ) contours on MRIs with and without fat saturation (±FS), and compare MRIs from different vendor MRI systems.
methodsT2-weighted (T2) and fat-saturated (T2FS) MRIs were acquired on 3T GE (GE, Waukesha, WI, USA) and Siemens (Erlangen, Germany) systems. Manual prostate and PZ contours were used to create atlas libraries. As a test MRI is entered, the procedure for atlas segmentation automatically identifies the atlas subjects that best match the test subject, followed by a normalized intensity-based free-form deformable registration. The contours are transformed to the test subject, and Dice similarity coefficients (DSC) and Hausdorff distances between atlas-generated and manual contours were used to assess performance.
resultsThree atlases were generated based on GE_T2 (n = 30), GE_T2FS (n = 30), and Siem_T2FS (n = 31). When test images matched the contrast and vendor of the atlas, DSCs of 0.81 and 0.83 for T2 ± FS were obtained (baseline performance). Atlases performed with higher accuracy when segmenting (i) T2FS vs. T2 images, likely due to a superior contrast between prostate vs. surrounding tissue; (ii) prostate vs. zonal anatomy; (iii) in the mid-gland vs. base and apex. Atlases performance declined when tested with images with differing contrast and MRI vendor. Conversely, combined atlases showed similar performance to baseline.
conclusionThe MRI atlas-based segmentation method achieved good results for prostate, PZ, and TZ compared to expert contoured volumes. Combined atlases performed similarly to matching atlas and scan type. The technique is fast, fully automatic, and implemented on commercially available clinical platform.
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